Measles is a serious infectious disease, with a global annual burden of approximately 30 million cases and about a million deaths. The vast majority of this burden falls on Africa. An effective vaccine has been available for over fifty years, but the coverage of measles immunization in Africa lags behind the rest of the world. Due to weaknesses in the World Health Organization's cluster sampling surveys of immunization coverage, determinants are best gleaned from national sample surveys, such as the Demographic and Health Surveys (DHS). Previous studies show that covariates of immunization, exist at all levels in the hierarchy of such data; namely at the level of children, mothers, families and communities. There has been little analysis of such data taking into account this hierarchical structure. Multilevel models allow for the design effect of such studies and produce estimates that are less biased. This study used data from nine sub-Saharan African DHS countries to investigate possible determinants of measles immunization. Models using standard logistic regression were compared to multilevel models. The multilevel models identified the same major determinants of children's measles immunization status, as found in the standard models. However, the multilevel models were more conservative in that they often made the fixed-effects standard errors larger. This sometimes led to reductions in the number of parameters in the fitted models. In particular, the multilevel models tended to reduce the number of variables at the higher levels of the hierarchy of the data, such as the variable identifying which region the child lived in. With respect to the variable of the distance to the nearest immunization facility, using the standard logistic regression model this parameter was significant in four of the five countries where it was measured. However, when the multilevel model was applied to this same data, the variable remained statistically significant in just one of the countries, Mali. Multilevel modeling techniques appear to be better suited than standard regression techniques when questions concerning resource availability or allocation for populations are important. In the application of multilevel models, it is common and perhaps best practice, to first examine data using standard logistic regression, which allows for the assessment of the goodness of fit of the parameters. The subsequent use of multilevel models, give more realistic estimates of the model parameters and their standard errors that take into account the structure of the data

Measles is a serious infectious disease, with a global annual burden of approximately 30 million cases and about a million deaths. The vast majority of this burden falls on Africa. An effective vaccine has been available for over fifty years, but the coverage of measles immunization in Africa lags behind the rest of the world. Due to weaknesses in the World Health Organization's cluster sampling surveys of immunization coverage, determinants are best gleaned from national sample surveys, such as the Demographic and Health Surveys (DHS). Previous studies show that covariates of immunization, exist at all levels in the hierarchy of such data; namely at the level of children, mothers, families and communities. There has been little analysis of such data taking into account this hierarchical structure. Multilevel models allow for the design effect of such studies and produce estimates that are less biased. This study used data from nine sub-Saharan African DHS countries to investigate possible determinants of measles immunization. Models using standard logistic regression were compared to multilevel models. The multilevel models identified the same major determinants of children's measles immunization status, as found in the standard models. However, the multilevel models were more conservative in that they often made the fixed-effects standard errors larger. This sometimes led to reductions in the number of parameters in the fitted models. In particular, the multilevel models tended to reduce the number of variables at the higher levels of the hierarchy of the data, such as the variable identifying which region the child lived in. With respect to the variable of the distance to the nearest immunization facility, using the standard logistic regression model this parameter was significant in four of the five countries where it was measured. However, when the multilevel model was applied to this same data, the variable remained statistically significant in just one of the countries, Mali. Multilevel modeling techniques appear to be better suited than standard regression techniques when questions concerning resource availability or allocation for populations are important. In the application of multilevel models, it is common and perhaps best practice, to first examine data using standard logistic regression, which allows for the assessment of the goodness of fit of the parameters. The subsequent use of multilevel models, give more realistic estimates of the model parameters and their standard errors that take into account the structure of the data